ggplot(total_hom_by_year, aes(x = year, y = total_hom)) +geom_line() +labs(title ="Total Homicides per 100,000 People (1990-2021)",x ="Year",y ="Total Homicides per 100,000 People")
ggplot(southamerica, aes(x = year, y = hom, group = country, color = country)) +geom_line() +labs(title ="Homicides per 100,000 in South American Countries (1990-2021)",x ="Year",y ="Ln Homicides per 100,000 people") +theme_minimal()
ggplot(southamerica, aes(x = year, y = homale, group = country, color = country)) +geom_line() +labs(title ="Homicides of males per 100,000 in South American Countries (1990-2021)",x ="Year",y ="Ln Homicides of males per 100,000 people") +theme_minimal()
Chile show an increase in homicide in recent years
ggplot(chl_flt, aes(x = year, y = hom, group = country, color = country)) +geom_line() +labs(title ="Homicides per 100,000 in Chile (1990-2021)",x ="Year",y ="Ln Homicides per 100,000 people") +theme_minimal()
ggplot(chl_flt, aes(x = year, y = homale, group = country, color = country)) +geom_line() +labs(title ="Homicides of males per 100,000 in Chile (1990-2021)",x ="Year",y ="Ln Homicides of males per 100,000 people") +theme_minimal()
Synthetic control: Could the 2019 policy, and the consequent migration flow, be related to increasing violence in Chile?
Data wrangling in Stata to create natural logarithm variables.
Synthetic control done in R.
Limitations
Lack of literature review.
Imputation on MICE withouth any restriction. In some cases, some variables have only few observations.
Potential collinearity, heteroskedasticity, and endogeneity.
Poverty variable left outside the analysis as imputed values extremely high for some countries.
Dependent variable accounts only for homicides of males but not for other crimes that could be helpful to analize: theft, drug related, organized crime.